Systems and methods for optimizing a network based on weather events

ABSTRACT

A device may receive historical network behavior data associated with a network that includes network devices and may receive historical weather data associated with a geographical location of the network. The device may receive historical action data identifying historical actions taken for the network in response to the historical weather data, and may train a correlation model, with the historical network behavior data, the historical weather data, and the historical action data, to generate a trained correlation model. The device may receive a weather event forecast associated with the geographical location, and may process the weather event forecast, with the trained correlation model, to determine an anticipated behavior of the network in response to the weather event forecast. The device may process data identifying the anticipated behavior, with the trained correlation model, to identify actions to take in response to the anticipated behavior and may perform the actions.

BACKGROUND

Weather events (e.g., tropical storms, hurricanes, tornados, blizzards,high winds, lightning, and/or the like) may cause power outages, downedutility poles, failed communication equipment, etc., in a geographicallocation. Many consumers in the geographical location, subject toextreme weather events, may utilize Internet service provider (ISP)routers to access both voice and data networks (e.g., the Internet), butweather events that cause problems, such as power outages, may preventaccess to such ISP routers and the respective networks.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1E are diagrams of an example associated with utilizing amachine learning model to predict network behavior based on weatherevents.

FIG. 2 is a diagram illustrating an example of training and using amachine learning model in connection with predicting network behaviorbased on weather events.

FIG. 3 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 4 is a diagram of example components of one or more devices of FIG.3.

FIG. 5 is a flowchart of an example process for utilizing a machinelearning model to predict network behavior based on weather events.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

When a power outage occurs in a geographical location due to a weatherevent, consumers utilizing ISP routers may lose power and connectivitywith the data network that relies on a power grid for service. As aresult of the power loss, consumers may then be forced to utilize acellular network (e.g., a long term evolution (LTE) or fourth generation(4G) network, a fifth generation (5G) network, and/or the like) toconnect to the data network. During the power outage period, a cellularnetwork provider may experience a dramatic increase in traffic volumebased on a time of day and/or a day of a week associated with the poweroutage, consumer density in the geographical location, a duration of thepower outage, and/or the like. The dramatic increase in traffic volumemay cause a rapid increase in network latency, capacity, etc., which maylead to degraded network performance. Thus, current techniques forhandling increased network utilization during a power outage may wastecomputing resources (e.g., processing resources, memory resources,communication resources, and/or the like), networking resources, humanresources, and/or the like associated with the network operatinginefficiently, identifying network resources to allocate for theincreased network utilization, handling consumer complaints associatedwith the network, and/or the like.

Some implementations described herein provide a system that utilizes amachine learning model to predict network behavior based on weatherevents. For example, the system may receive historical network behaviordata associated with a network that includes network devices and mayreceive historical weather data associated with a geographical locationassociated with the network. The system may receive historical actiondata identifying historical actions taken for the network in response tothe historical weather data, and may train a correlation model, with thehistorical network behavior data, the historical weather data, and thehistorical action data, to generate a trained correlation model. Thesystem may receive a weather event forecast associated with thegeographical location and may process the weather event forecast, withthe trained correlation model, to determine an anticipated behavior ofthe network in response to the weather event forecast. The system mayprocess data identifying the anticipated behavior of the network, withthe trained correlation model, to identify one or more actions to takein response to the anticipated behavior and may perform the one or moreactions.

In this way, the system utilizes a machine learning model to predictnetwork behavior based on weather events. The system may receivehistorical network behavior data identifying behaviors of a networkduring power outages and historical weather data identifying weatherthat causes the power outages. The system may train the machine learningmodel based on the historical network behavior data and the historicalweather data. The system may utilize the trained machine learning modelto predict power outages and network behavior based on weather eventforecasts. The system may determine one or more actions to perform withthe network when a power outage is predicted. Thus, the system conservescomputing resources, networking resources, human resources, and/or thelike associated with the network operating inefficiently, identifyingnetwork resources to allocate for the increased network utilization,handling consumer complaints associated with the network, and/or thelike.

FIGS. 1A-1E are diagrams of an example 100 associated with utilizing amachine learning model to predict network behavior based on weatherevents. As shown in FIGS. 1A-1E, example 100 includes a network 105,network devices 110 within network 105, and a prediction system 115.Network 105 may include a radio access network (RAN) associated with anLTE or 4G network, a 5G network, and/or the like. Each network device110 may include an eNodeB (eNB) capable of transferring traffic, such asaudio, video, text, and/or other traffic associated with network 105, agNodeB (gNB) that supports, for example, a cellular radio accesstechnology (RAT) and wireless communication for network 105, and/or thelike. Prediction system 115 may include a system that utilizes a machinelearning model to predict network behavior based on weather events.Prediction system 115 may be included within network 105 or may beseparate from network 105.

As shown in FIG. 1A, and by reference number 120, prediction system 115may receive historical network behavior data associated with network 105that includes network devices 110. The historical network behavior datamay include historical data identifying bandwidth utilizations bynetwork 105, capacity of network 105, transport latencies associatedwith network 105, packet latencies associated with network 105, packetjitters associated with network 105, capacities of network devices 110,bandwidth utilizations of network devices 110, user behavior associatedwith network 105 during a power outage, customer issues identified basedon the user behavior, and/or the like. Prediction system 115 may receivethe historical network behavior data and may store the historicalnetwork behavior data in a data structure (e.g., a database, a table, alist, and/or the like) associated with prediction system 115. In someimplementations, prediction system 115 provides, to network 105 and/ornetwork devices 110, a request for the historical network behavior data,and receives the historical network behavior data from network 105and/or network devices 110 based on the request. In someimplementations, prediction system 115 periodically receives thehistorical network behavior data, continuously receives the historicalnetwork behavior data, and/or the like.

As further shown in FIG. 1A, and by reference number 125, predictionsystem 115 may receive historical weather data associated with ageographical location associated with network 105. The historicalweather data may correspond with a same or similar time periodassociated with the historical network behavior data. The historicalweather data may include data associated with the geographical location,wind speeds and directions associated with the geographical location,precipitation amounts associated with the geographical location,lightning strikes associated with the geographical location, a storm(e.g., a hurricane, a tornado, a snowstorm, an ice storm, and/or thelike) associated with the geographical location, and/or the like.Prediction system 115 may receive the historical weather data and maystore the historical weather data in the data structure associated withprediction system 115. In some implementations, prediction system 115provides, to a device associated with a weather service for thegeographical location, a request for the historical weather data, andreceives the historical weather data from the device associated with theweather service based on the request. In some implementations,prediction system 115 periodically receives the historical weather data,continuously receives the historical weather data, and/or the like.

As further shown in FIG. 1A, and by reference number 130, predictionsystem 115 may receive historical action data identifying historicalactions taken for network 105 in response to the historical weatherdata. The historical action data may include data identifyingadjustments to parameters associated with network 105 and/or networkdevices 110 based on the historical weather data, capacity requirementupgrades made to network 105 and/or network devices 110 based on thehistorical weather data, additional network devices 110 added to networkbased on the historical weather data, and/or the like. Prediction system115 may receive the historical action data and may store the historicalaction data in the data structure associated with prediction system 115.In some implementations, prediction system 115 provides, to network 105and/or network devices 110, a request for the historical action data,and receives the historical action data from network 105 and/or networkdevices 110 based on the request. In some implementations, predictionsystem 115 periodically receives the historical action data,continuously receives the historical action data, and/or the like.

As shown in FIG. 1B, and by reference number 135, prediction system 115may train a correlation model, with the historical network behavior data(e.g., user behavior associated with network 105 during a power outageand not during a power outage, customer issues identified based on theuser behavior, and/or the like), the historical weather data, and thehistorical action data, to generate a trained correlation model. Thecorrelation model may include a machine learning model, such as a deeplearning model. In some implementations, prediction system 115 trainsthe correlation model to predict power outages for the geographicallocation based on the historical weather data, anticipated behaviors ofnetwork 105 (e.g., low bandwidths of network 105, high utilization ofnetwork 105, low capacities of network 105, and/or the like) based onthe predicted power outages, responses to the predicted power outagesbased on the predicted anticipated behaviors of network 105, and/or thelike. In some implementations, the correlation model may be trained byanother device and received by prediction system 115 from the otherdevice. For example, prediction system 115 may provide the historicalnetwork behavior data, the historical weather data, and the historicalaction data to the other device, and the other device may train thecorrelation model based on the historical network behavior data, thehistorical weather data, and the historical action data, to generate thetrained correlation model. The other device may then provide the trainedcorrelation model to prediction system 115. Further details of trainingthe correlation model are described below in connection with FIG. 2.

As shown in FIG. 1C, and by reference number 140, prediction system 115may receive a weather event forecast associated with the geographicallocation. The weather event forecast may include multi-day forecasts ofweather (e.g., radar, wind speeds and directions, precipitation amounts,lightning strikes, storms, and/or the like) associated with thegeographical location. In some implementations, prediction system 115receives the weather event forecast from the device associated with theweather service for the geographical location.

As further shown in FIG. 1C, and by reference number 145, predictionsystem 115 may process the weather event forecast, with the trainedcorrelation model, to determine an anticipated behavior of network 105in response to the weather event forecast. The anticipated behavior ofnetwork 105 may include increased utilization of network 105, reducedcapacity of network 105, increased transport latency associated withnetwork 105, increased packet latency associated with network 105,increased packet jitter associated with network 105, reduced capacitiesof network devices 110, increased bandwidth utilizations of networkdevices 110, and/or the like.

In some implementations, the trained correlation model may determinethat the anticipated behavior of network 105 is associated with a poweroutage for the geographical location when the anticipated behavior ofnetwork 105 satisfies a threshold (e.g., a threshold utilization ofnetwork 105, a threshold capacity of network 105, a threshold transportlatency associated with network 105, a threshold packet latencyassociated with network 105, packet jitter associated with network 105,threshold capacities of network devices 110, threshold bandwidthutilizations of network devices 110, and/or the like). For example, thetrained correlation model may distinguish rapid rises in utilization ofnetwork 105 caused by faulty equipment or other anomalies from rapidrises in utilization of network 105 caused by actual power failures. Thetrained correlation model may determine that the anticipated behavior ofnetwork 105 is not associated with a power outage for the geographicallocation when the anticipated behavior of network 105 fails to satisfythe threshold. For example, the trained correlation model may determinethat specific heavy weather events (e.g., wind speeds greater than fiftymiles per hour, multiple lightning strikes per minute, and/or the like),within the geographical location, cause above-normal activity and stresswithin network 105, such as increased utilization of bandwidth ofnetwork 105, increased transport latencies of network 105, and/or thelike.

In some implementations, the trained correlation model may determinethat the anticipated behavior of network 105 is associated with a poweroutage for the geographical location and analyze network utilizationdata associated with one or more other networks geographically locatedproximate to network 105. The trained correlation model may determine anextent of the power outage based on analyzing the network utilizationdata and may determine the anticipated behavior of network 105 based onthe extent of the power outage.

As shown in FIG. 1D, and by reference number 150, prediction system 115may process data identifying the anticipated behavior of network 105,with the trained correlation model, to identify one or more actions totake in response to the anticipated behavior. The one or more actionsmay include actions to increase bandwidth of network 105, increasecapacity of network 105, decrease transport latency associated withnetwork 105, decrease packet latency associated with network 105,decrease packet jitter associated with network 105, increase capacitiesof network devices 110, increase bandwidths of network devices 110,and/or the like. For example, the one or more actions may includeprediction system 115 causing one or more additional network devices 110to be temporarily allocated for the geographical location; causing anautonomous vehicle with a network device 110 to be dispatched to thegeographical location; providing a notification about the anticipatedbehavior to users of network 105 located in the geographical location;causing an order to be placed for a new network device 110, adjustingone or more parameters of one or more of network devices 110 based onthe anticipated behavior; implement an upgrade to network 105; and/orthe like. Further details of the one or more actions are described belowin connection with FIG. 1E.

As shown in FIG. 1E, and by reference number 155, prediction system 115may perform the one or more actions. In some implementations, performingthe one or more actions includes prediction system 115 causing one ormore additional network devices 110 to be temporarily allocated for thegeographical location. For example, prediction system 115 may causeadditional network devices 110, associated with other networks proximateto the geographical location, to be temporarily allocated for thegeographical location. In this way, network 105 and the one or moreadditional network devices 110 may handle an increase in bandwidthutilization of network 105, a decrease in capacity of network 105,and/or the like. This may conserve computing resources, networkingresources, human resources, and/or the like associated with network 105operating inefficiently, identifying network resources to allocate forthe increased network utilization, handling consumer complaintsassociated with network 105, and/or the like.

In some implementations, performing the one or more actions includesprediction system 115 causing an autonomous vehicle (e.g., a robot, anunmanned aerial vehicle, an autonomous car, an autonomous truck, and/orthe like) equipped with a network device 110 (e.g., an eNodeB, a gNodeB,and/or the like) to be dispatched to the geographical location. Networkdevice 110 of the autonomous vehicle may provide additional service tousers in the geographical location. In this way, network 105 and thenetwork device 110 of autonomous vehicle may handle an increase inbandwidth utilization of network 105, a decrease in capacity of network105, and/or the like. This may conserve computing resources, networkingresources, human resources, and/or the like associated with network 105operating inefficiently, identifying network resources to allocate forthe increased network utilization, handling consumer complaintsassociated with network 105, and/or the like.

In some implementations, performing the one or more actions includesprediction system 115 generating a notification (e.g., a text message,an email message, an alert message, an automated telephone call, and/orthe like) about the anticipated behavior of network 105 and providingthe notification to users of network 105 located in the geographicallocation. In this way, the users of network 105 may be notified about anincrease in bandwidth utilization of network 105, a decrease in capacityof network 105, and/or the like. This may conserve computing resources,networking resources, human resources, and/or the like associated withhandling consumer complaints associated with network 105.

In some implementations, performing the one or more actions includesprediction system 115 causing an order to be placed for a new networkdevice 110 of network 105 based on the anticipated behavior of thenetwork. For example, prediction system 115 may determine that at leastone additional network device 110 is needed whenever a power outageoccurs in the geographical location. Thus, prediction system 115 mayorder the new network device 110 for network 105 in order to handlenetwork demands associated with future power outages. In this way,network 105 and the new network device 110 may handle an increase inbandwidth utilization of network 105, a decrease in capacity of network105, and/or the like associated with future power outages. This mayconserve computing resources, networking resources, human resources,and/or the like associated with network 105 operating inefficiently,identifying network resources to allocate for the increased networkutilization, handling consumer complaints associated with network 105,and/or the like.

In some implementations, performing the one or more actions includesprediction system 115 adjusting one or more parameters of one or more ofnetwork devices 110 based on the anticipated behavior of network 105.For example, prediction system 115 may adjust parameters of the one ormore network devices 110 (e.g., a tilt angle of a base station, a powerof a signal generated by a base station, and/or the like) so that theone or more network devices 110 may handle an increase in bandwidthutilization of network 105, a decrease in capacity of network 105,and/or the like. This, in turn, may conserve computing resources,networking resources, human resources, and/or the like associated withnetwork 105 operating inefficiently, identifying network resources toallocate for the increased network utilization, handling consumercomplaints associated with network 105, and/or the like.

In some implementations, performing the one or more actions includesprediction system 115 determining a network capacity requirement basedon the anticipated behavior of network 105 and implementing an upgradeto network 105 based on the network capacity requirement. For example,prediction system 115 may determine that at least one additional networkdevice 110 is needed whenever a power outage occurs in the geographicallocation based on a network capacity requirement of network 105 duringthe power outage. Thus, prediction system 115 may cause a new networkdevice 110 to be provided for network 105 to handle the network capacityrequirement of network 105 during power outages. This may conservecomputing resources, networking resources, human resources, and/or thelike associated with network 105 operating inefficiently, identifyingnetwork resources to allocate for the increased network utilization,handling consumer complaints associated with network 105, and/or thelike.

In some implementations, performing the one or more actions includesprediction system 115 retraining the correlation model based on theanticipated behavior of network 105. Prediction system 115 may utilizethe anticipated behavior of network 105 as additional training data forretraining the correlation model, thereby increasing the quantity oftraining data available for training the correlation model. Accordingly,prediction system 115 may conserve computing resources associated withidentifying, obtaining, and/or generating historical data for trainingthe correlation model relative to other systems for identifying,obtaining, and/or generating historical data for training machinelearning models.

In some implementations, performing the one or more actions includesprediction system 115 prioritizing of select customers within thegeographical location of the power outage. For example, the selectcustomers may pay for premium latency sensitive services from network105, and prediction system 115 may ensure that the select customers areallocated resources of network 105 for the premium latency sensitiveservices (e.g., during a power outage) before other customers that didnot pay for the premium latency sensitive services. The latencysensitive services may be defined by service level agreements (SLAs)between the select customers and a carrier and may include requirementsassociated with a power outage in the geographical location. Therequirements may include improving qualities of service for the selectcustomers by updating, reconfiguring, provisioning, and/or the likenetwork devices 110 located geographical location; activating networkdevices 110 that are dormant until a power outage occurs and which areprovisioned to only accept network traffic from the select customers;and/or the like. In this way, the select customers may still be providedthe premium latency sensitive services even during a power outage, whichmay conserve computing resources, networking resources, human resources,and/or the like for handling consumer complaints associated with network105.

In some implementations, performing the one or more actions includesprediction system 115 determining a power outage signature based on theanticipated behavior of network 105, and utilizing the power outagesignature as another alarm or alert mechanism in case power supplyalarms of network devices 110 fail to trigger during a power outage. Forexample, network devices 110 typically generate alarms which may bereceived and reported by monitoring systems of network 105 when poweroutages occur. These alarms may fail to be detected by the monitoringsystems due to hardware issues associated with network devices 110,software issues associated with the monitoring systems, and/or the like.Prediction system 115 may serve as a backup power outage alarm (e.g., alogical and virtual power failure detector) since prediction system 115may learn to detect power outage conditions and a scope or geographicarea of the power outage. This may conserve computing resources,networking resources, human resources, and/or the like associated withnetwork 105 operating inefficiently, identifying network resources toallocate for the increased network utilization, handling consumercomplaints associated with network 105, and/or the like.

In some implementations, performing the one or more actions includesprediction system 115 determining usage patterns associated with network105 during a power outage in the geographical location, and modifyingnetwork 105 based on the usage patterns. For example, prediction system115 may determine that a portion of network 105 requires an additionalnetwork device 110 based on the usage patterns of network 105 during apower outage. Prediction system 115 may cause a new network device 110to be allocated for the portion of network 105. This may conservecomputing resources, networking resources, human resources, and/or thelike associated with network 105 operating inefficiently, identifyingnetwork resources to allocate for the increased network utilization,handling consumer complaints associated with network 105, and/or thelike.

In this way, prediction system 115 utilizes a machine learning model topredict network behavior based on weather events. Prediction system 115may receive historical network behavior data identifying behaviors ofnetwork 105 during power outages, and historical weather dataidentifying weather that causes the power outages. Prediction system 115may train the machine learning model based on the historical networkbehavior data and the historical weather data and may utilize thetrained machine learning model to predict power outages and networkbehavior based on weather event forecasts. Prediction system 115 maydetermine one or more actions to perform with network 105 when a poweroutage is predicted. Thus, prediction system 115 conserves computingresources, networking resources, human resources, and/or the likeassociated with network 105 operating inefficiently, identifying networkresources to allocate for the increased network utilization, handlingconsumer complaints associated with network 105, and/or the like.

As indicated above, FIGS. 1A-1E are provided as an example. Otherexamples may differ from what is described with regard to FIGS. 1A-1E.The number and arrangement of devices shown in FIGS. 1A-1E are providedas an example. In practice, there may be additional devices, fewerdevices, different devices, or differently arranged devices than thoseshown in FIGS. 1A-1E. Furthermore, two or more devices shown in FIGS.1A-1E may be implemented within a single device, or a single deviceshown in FIGS. 1A-1E may be implemented as multiple, distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) shown in FIGS. 1A-1E may perform one or more functionsdescribed as being performed by another set of devices shown in FIGS.1A-1E.

FIG. 2 is a diagram illustrating an example 200 of training and using amachine learning model (e.g., the optimized-based poisoning model or thestatistical-based poisoning model) in connection with predicting networkbehavior based on weather events. The machine learning model trainingand usage described herein may be performed using a machine learningsystem. The machine learning system may include or may be included in acomputing device, a server, a cloud computing environment, and/or thelike, such as prediction system 115 described in more detail elsewhereherein.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations (e.g.,bandwidth utilizations by network 105, capacity of network 105,transport latencies associated with network 105, packet latenciesassociated with network 105, packet jitters associated with network 105,and/or the like) may be obtained from historical data, such as datagathered during one or more processes described herein. In someimplementations, the machine learning system may receive the set ofobservations (e.g., as input) from prediction system 115, as describedelsewhere herein.

As shown by reference number 210, the set of observations includes afeature set. The feature set may include a set of variables, and avariable may be referred to as a feature. A specific observation mayinclude a set of variable values (or feature values) corresponding tothe set of variables. In some implementations, the machine learningsystem may determine variables for a set of observations and/or variablevalues for a specific observation based on input received fromprediction system 115. For example, the machine learning system mayidentify a feature set (e.g., one or more features and/or featurevalues) by extracting the feature set from structured data, byperforming natural language processing to extract the feature set fromunstructured data, by receiving input from an operator, and/or the like.

As an example, a feature set for a set of observations may include afirst feature of historical network behavior data, a second feature ofhistorical weather data, a third feature of historical action data, andso on. As shown, for a first observation, the first feature may have avalue of network behavior 1, the second feature may have a value ofweather data 1, the third feature may have a value of action 1, and soon. These features and feature values are provided as examples and maydiffer in other examples.

As shown by reference number 215, the set of observations may beassociated with a target variable. The target variable may represent avariable having a numeric value, may represent a variable having anumeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiple classes, classifications,labels, and/or the like), may represent a variable having a Booleanvalue, and/or the like. A target variable may be associated with atarget variable value, and a target variable value may be specific to anobservation. In example 200, the target variable is an anticipatednetwork behavior, which has a value of anticipated network behavior 1for the first observation.

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable. This may bereferred to as an unsupervised learning model. In this case, the machinelearning model may learn patterns from the set of observations withoutlabeling or supervision, and may provide output that indicates suchpatterns, such as by using clustering and/or association to identifyrelated groups of items within the set of observations.

As shown by reference number 220, the machine learning system may traina machine learning model using the set of observations and using one ormore machine learning algorithms, such as a regression algorithm, adecision tree algorithm, a neural network algorithm, a k-nearestneighbor algorithm, a support vector machine algorithm, and/or the like.After training, the machine learning system may store the machinelearning model as a trained machine learning model 225 to be used toanalyze new observations.

As shown by reference number 230, the machine learning system may applythe trained machine learning model 225 to a new observation, such as byreceiving a new observation and inputting the new observation to thetrained machine learning model 225. As shown, the new observation mayinclude a first feature of network behavior X, a second feature ofweather data X, a third feature of action X, and so on, as an example.The machine learning system may apply the trained machine learning model225 to the new observation to generate an output (e.g., a result). Thetype of output may depend on the type of machine learning model and/orthe type of machine learning task being performed. For example, theoutput may include a predicted value of a target variable, such as whensupervised learning is employed. Additionally, or alternatively, theoutput may include information that identifies a cluster to which thenew observation belongs, information that indicates a degree ofsimilarity between the new observation and one or more otherobservations, and/or the like, such as when unsupervised learning isemployed.

As an example, the trained machine learning model 225 may predictanticipated network behavior X for the target variable of theanticipated network behavior for the new observation, as shown byreference number 235. Based on this prediction, the machine learningsystem may provide a first recommendation, may provide output fordetermination of a first recommendation, may perform a first automatedaction, may cause a first automated action to be performed (e.g., byinstructing another device to perform the automated action), and/or thelike.

In some implementations, the trained machine learning model 225 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 240. The observations within a cluster may have athreshold degree of similarity. As an example, if the machine learningsystem classifies the new observation in a first cluster (e.g., anetwork behavior data cluster), then the machine learning system mayprovide a first recommendation. Additionally, or alternatively, themachine learning system may perform a first automated action and/or maycause a first automated action to be performed (e.g., by instructinganother device to perform the automated action) based on classifying thenew observation in the first cluster.

As another example, if the machine learning system were to classify thenew observation in a second cluster (e.g., a weather data cluster), thenthe machine learning system may provide a second (e.g., different)recommendation and/or may perform or cause performance of a second(e.g., different) automated action.

In some implementations, the recommendation and/or the automated actionassociated with the new observation may be based on a target variablevalue having a particular label (e.g., classification, categorization,and/or the like), may be based on whether a target variable valuesatisfies one or more thresholds (e.g., whether the target variablevalue is greater than a threshold, is less than a threshold, is equal toa threshold, falls within a range of threshold values, and/or the like),may be based on a cluster in which the new observation is classified,and/or the like.

In this way, the machine learning system may apply a rigorous andautomated process to predict network behavior based on weather events.The machine learning system enables recognition and/or identification oftens, hundreds, thousands, or millions of features and/or feature valuesfor tens, hundreds, thousands, or millions of observations, therebyincreasing accuracy and consistency and reducing delay associated withpredicting network behavior based on weather events relative torequiring computing resources to be allocated for tens, hundreds, orthousands of operators to manually predict network behavior based onweather events.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2.

FIG. 3 is a diagram of an example environment 300 in which systemsand/or methods described herein may be implemented. As shown in FIG. 3,environment 300 may include a prediction system 115, which may includeone or more elements of and/or may execute within a cloud computingsystem 302. The cloud computing system 302 may include one or moreelements 303-313, as described in more detail below. As further shown inFIG. 3, environment 300 may include network 105 and/or network device110. Devices and/or elements of environment 300 may interconnect viawired connections and/or wireless connections.

Network 105 may include a RAN that includes one or more network device110 that take the form of eNBs, gNBs, and/or the like, via which a userdevice (e.g., a mobile phone, a laptop computer, a tablet computer, adesktop computer, and/or the like) communicates with a core network.Network 105 may include one or more wired and/or wireless networks. Forexample, network 105 may include a cellular network (e.g., a 5G network,an LTE network, a 3G network, a code division multiple access (CDMA)network, etc.), a public land mobile network (PLMN), a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), a telephone network (e.g., the Public Switched Telephone Network(PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, and/or the like, and/or acombination of these or other types of networks.

Network device 110 includes one or more devices capable of transferringtraffic, such as audio, video, text, and/or other traffic, destined forand/or received from a UE. In some implementations, network device 110may include an eNB associated with an LTE network that receives trafficfrom and/or sends traffic to a core network. Additionally, oralternatively, network device 110 may include a gNB associated with aRAN of a 5G network. Network device 110 may send traffic to and/orreceive traffic from a UE via an air interface, such as a cellular RAT.In some implementations, network device 110 may include a basetransceiver station, a radio base station, a base station subsystem, acellular site, a cellular tower, an access point, a transmit receivepoint (TRP), a radio access node, a macrocell base station, a microcellbase station, a picocell base station, a femtocell base station, acellular-site router, an aggregation router, a core network device, andother network entities that can support wireless communication. Networkdevice 110 may transfer traffic between UEs (e.g., using a cellularRAT), one or more other network devices 110 (e.g., using a wirelessinterface or a backhaul interface, such as a wired backhaul interface),and/or a core network. Network device 110 may provide one or more cellsthat cover geographic areas.

Cloud computing system 302 includes computing hardware 303, a resourcemanagement component 304, a host operating system (OS) 305, and/or oneor more virtual computing systems 306. The resource management component304 may perform virtualization (e.g., abstraction) of computing hardware303 to create the one or more virtual computing systems 306. Usingvirtualization, the resource management component 304 enables a singlecomputing device (e.g., a computer, a server, and/or the like) tooperate like multiple computing devices, such as by creating multipleisolated virtual computing systems 306 from computing hardware 303 ofthe single computing device. In this way, computing hardware 303 canoperate more efficiently, with lower power consumption, higherreliability, higher availability, higher utilization, greaterflexibility, and lower cost than using separate computing devices.

Computing hardware 303 includes hardware and corresponding resourcesfrom one or more computing devices. For example, computing hardware 303may include hardware from a single computing device (e.g., a singleserver) or from multiple computing devices (e.g., multiple servers),such as multiple computing devices in one or more data centers. Asshown, computing hardware 303 may include one or more processors 307,one or more memories 308, one or more storage components 309, and/or oneor more networking components 310. Examples of a processor, a memory, astorage component, and a networking component (e.g., a communicationcomponent) are described elsewhere herein.

The resource management component 304 includes a virtualizationapplication (e.g., executing on hardware, such as computing hardware303) capable of virtualizing computing hardware 303 to start, stop,and/or manage one or more virtual computing systems 306. For example,the resource management component 304 may include a hypervisor (e.g., abare-metal or Type 1 hypervisor, a hosted or Type 2 hypervisor, and/orthe like) or a virtual machine monitor, such as when the virtualcomputing systems 306 are virtual machines 311. Additionally, oralternatively, the resource management component 304 may include acontainer manager, such as when the virtual computing systems 306 arecontainers 312. In some implementations, the resource managementcomponent 304 executes within and/or in coordination with a hostoperating system 305.

A virtual computing system 306 includes a virtual environment thatenables cloud-based execution of operations and/or processes describedherein using computing hardware 303. As shown, a virtual computingsystem 306 may include a virtual machine 311, a container 312, a hybridenvironment 313 that includes a virtual machine and a container, and/orthe like. A virtual computing system 306 may execute one or moreapplications using a file system that includes binary files, softwarelibraries, and/or other resources required to execute applications on aguest operating system (e.g., within the virtual computing system 306)or the host operating system 305.

Although prediction system 115 may include one or more elements 303-313of the cloud computing system 302, may execute within the cloudcomputing system 302, and/or may be hosted within the cloud computingsystem 302, in some implementations, prediction system 115 may not becloud-based (e.g., may be implemented outside of a cloud computingsystem) or may be partially cloud-based. For example, prediction system115 may include one or more devices that are not part of the cloudcomputing system 302, such as device 400 of FIG. 4, which may include astandalone server or another type of computing device. Prediction system115 may perform one or more operations and/or processes described inmore detail elsewhere herein.

The number and arrangement of devices and networks shown in FIG. 3 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 3. Furthermore, two or more devices shown in FIG. 3 may beimplemented within a single device, or a single device shown in FIG. 3may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 300 may perform one or more functions described as beingperformed by another set of devices of environment 300.

FIG. 4 is a diagram of example components of a device 400, which maycorrespond to prediction system 115 and/or network device 110. In someimplementations, prediction system 115 and/or network device 110 mayinclude one or more devices 400 and/or one or more components of device400. As shown in FIG. 4, device 400 may include a bus 410, a processor420, a memory 430, a storage component 440, an input component 450, anoutput component 460, and a communication component 470.

Bus 410 includes a component that enables wired and/or wirelesscommunication among the components of device 400. Processor 420 includesa central processing unit, a graphics processing unit, a microprocessor,a controller, a microcontroller, a digital signal processor, afield-programmable gate array, an application-specific integratedcircuit, and/or another type of processing component. Processor 420 isimplemented in hardware, firmware, or a combination of hardware andsoftware. In some implementations, processor 420 includes one or moreprocessors capable of being programmed to perform a function. Memory 430includes a random access memory, a read only memory, and/or another typeof memory (e.g., a flash memory, a magnetic memory, and/or an opticalmemory).

Storage component 440 stores information and/or software related to theoperation of device 400. For example, storage component 440 may includea hard disk drive, a magnetic disk drive, an optical disk drive, asolid-state disk drive, a compact disc, a digital versatile disc, and/oranother type of non-transitory computer-readable medium. Input component450 enables device 400 to receive input, such as user input and/orsensed inputs. For example, input component 450 may include a touchscreen, a keyboard, a keypad, a mouse, a button, a microphone, a switch,a sensor, a global positioning system component, an accelerometer, agyroscope, an actuator, and/or the like. Output component 460 enablesdevice 400 to provide output, such as via a display, a speaker, and/orone or more light-emitting diodes. Communication component 470 enablesdevice 400 to communicate with other devices, such as via a wiredconnection and/or a wireless connection. For example, communicationcomponent 470 may include a receiver, a transmitter, a transceiver, amodem, a network interface card, an antenna, and/or the like.

Device 400 may perform one or more processes described herein. Forexample, a non-transitory computer-readable medium (e.g., memory 430and/or storage component 440) may store a set of instructions (e.g., oneor more instructions, code, software code, program code, and/or thelike) for execution by processor 420. Processor 420 may execute the setof instructions to perform one or more processes described herein. Insome implementations, execution of the set of instructions, by one ormore processors 420, causes the one or more processors 420 and/or thedevice 400 to perform one or more processes described herein. In someimplementations, hardwired circuitry may be used instead of or incombination with the instructions to perform one or more processesdescribed herein. Thus, implementations described herein are not limitedto any specific combination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 4 are provided asan example. Device 400 may include additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 4. Additionally, or alternatively, a set ofcomponents (e.g., one or more components) of device 400 may perform oneor more functions described as being performed by another set ofcomponents of device 400.

FIG. 5 is a flowchart of an example process 500 associated withutilizing a machine learning model to predict network behavior based onweather events. In some implementations, one or more process blocks ofFIG. 4 may be performed by a device (e.g., prediction system 115). Insome implementations, one or more process blocks of FIG. 5 may beperformed by another device or a group of devices separate from orincluding the device, such as a network device (e.g., network device110). Additionally, or alternatively, one or more process blocks of FIG.5 may be performed by one or more components of device 400, such asprocessor 420, memory 430, storage component 440, input component 450,output component 460, and/or communication component 470.

As shown in FIG. 5, process 500 may include receiving historical networkbehavior data associated with a network that includes network devices(block 510). For example, the device may receive historical networkbehavior data associated with a network that includes network devices,as described above.

As further shown in FIG. 5, process 500 may include receiving historicalweather data associated with a geographical location associated with thenetwork (block 520). For example, the device may receive historicalweather data associated with a geographical location associated with thenetwork, as described above.

As further shown in FIG. 5, process 500 may include receiving historicalaction data identifying historical actions taken for the network inresponse to the historical weather data (block 530). For example, thedevice may receive historical action data identifying historical actionstaken for the network in response to the historical weather data, asdescribed above.

As further shown in FIG. 5, process 500 may include training acorrelation model, with the historical network behavior data, thehistorical weather data, and the historical action data, to generate atrained correlation model (block 540). For example, the device may traina correlation model, with the historical network behavior data, thehistorical weather data, and the historical action data, to generate atrained correlation model, as described above.

As further shown in FIG. 5, process 500 may include receiving a weatherevent forecast associated with the geographical location (block 550).For example, the device may receive a weather event forecast associatedwith the geographical location, as described above.

As further shown in FIG. 5, process 500 may include processing theweather event forecast, with the trained correlation model, to determinean anticipated behavior of the network in response to the weather eventforecast (block 560). For example, the device may process the weatherevent forecast, with the trained correlation model, to determine ananticipated behavior of the network in response to the weather eventforecast, as described above.

As further shown in FIG. 5, process 500 may include processing dataidentifying the anticipated behavior of the network, with the trainedcorrelation model, to identify one or more actions to take in responseto the anticipated behavior (block 570). For example, the device mayprocess data identifying the anticipated behavior of the network, withthe trained correlation model, to identify one or more actions to takein response to the anticipated behavior, as described above.

As further shown in FIG. 5, process 500 may include performing the oneor more actions (block 580). For example, the device may perform the oneor more actions, as described above.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the historical network behavior data includesdata identifying one or more of bandwidth utilizations by the network,transporting latencies associated with the network, packet latenciesassociated with the network, or packet jitters associated with thenetwork.

In a second implementation, alone or in combination with the firstimplementation, the historical weather data includes data identifyingone or more of radar associated with the geographical location, windingspeeds and directions associated with the geographical location,precipitation amounts associated with the geographical location,lightning strikes associated with the geographical location, or a stormassociated with the geographical location.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, training the correlation model,with the historical network behavior data, the historical weather data,and the historical action data, to generate the trained correlationmodel includes training the trained correlation model to predict poweroutages for the geographical location based on the historical weatherdata; training the trained correlation model to predict anticipatedbehaviors of the network based on the predicted power outages; andtraining the trained correlation model to predict responses to thepredicted power outages based on the predicted anticipated behaviors ofthe network.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the correlation model includesa deep learning model.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, processing the weather eventforecast, with the trained correlation model, to determine theanticipated behavior of the network in response to the weather eventforecast includes determining that the anticipated behavior of thenetwork is associated with a power outage for the geographical locationwhen the anticipated behavior of the network satisfies a threshold andnot when the anticipated behavior of the network fails to satisfy thethreshold.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, processing the weather eventforecast, with the trained correlation model, to determine theanticipated behavior of the network in response to the weather eventforecast includes determining that the anticipated behavior of thenetwork is associated with a power outage for the geographical location,analyzing network utilization data associated with one or more othernetworks geographically located proximate to the network; determining anextent of the power outage based on analyzing the network utilizationdata; and determining the anticipated behavior of the network based onthe extent of the power outage.

In a seventh implementation, alone or in combination with one or more ofthe first through sixth implementations, performing the one or moreactions includes one or more of causing one or more additional networkdevices to be temporarily allocated for the geographical location;causing an autonomous vehicle with a network device to be dispatched tothe geographical location; causing a technician to be dispatched to thegeographical location; or determining a power outage signature based onthe anticipated behavior of the network and utilizing the power outagesignature as an alert mechanism.

In an eighth implementation, alone or in combination with one or more ofthe first through seventh implementations, performing the one or moreactions includes generating a notification about the anticipatedbehavior of the network, and providing the notification to users of thenetwork located in the geographical location.

In a ninth implementation, alone or in combination with one or more ofthe first through eighth implementations, performing the one or moreactions includes determining a network capacity requirement for thenetwork based on the anticipated behavior of the network; determining anupgrade to the network based on the network capacity requirement; andcausing the upgrade to the network to be implemented.

In a tenth implementation, alone or in combination with one or more ofthe first through ninth implementations, performing the one or moreactions includes one or more of causing an order to be placed for a newnetwork device of the network based on the anticipated behavior of thenetwork; adjusting one or more parameters of one or more of the networkdevices based on the anticipated behavior of the network; retraining thecorrelation model based on the anticipated behavior of the network; orprioritizing select users of the network located in the geographicallocation and that have paid for latency sensitive services.

In an eleventh implementation, alone or in combination with one or moreof the first through tenth implementations, the network devices includeone or more of a cellular-site router, an eNodeB, a gNodeB, anaggregation router, or a device for a core network.

In a twelfth implementation, alone or in combination with one or more ofthe first through eleventh implementations, the historical networkbehavior data and the historical weather data are associated with acommon time period.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software. Itwill be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, it should be understood thatsuch information shall be used in accordance with all applicable lawsconcerning protection of personal information. Additionally, thecollection, storage, and use of such information can be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as can be appropriate for thesituation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set. As used herein, aphrase referring to “at least one of” a list of items refers to anycombination of those items, including single members. As an example, “atleast one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c,and a-b-c, as well as any combination with multiple of the same item.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, or a combination of related and unrelateditems), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

What is claimed is:
 1. A method, comprising: receiving, by a device,historical network behavior data associated with a network that includesnetwork devices; receiving, by the device, historical weather dataassociated with a geographical location associated with the network;receiving, by the device, historical action data identifying historicalactions taken for the network in response to the historical weatherdata; training, by the device, a correlation model, with the historicalnetwork behavior data, the historical weather data, and the historicalaction data, to generate a trained correlation model; receiving, by thedevice, a weather event forecast associated with the geographicallocation; processing, by the device, the weather event forecast, withthe trained correlation model, to determine an anticipated behavior ofthe network in response to the weather event forecast; processing, bythe device, data identifying the anticipated behavior of the network,with the trained correlation model, to identify one or more actions totake in response to the anticipated behavior; and performing, by thedevice, the one or more actions.
 2. The method of claim 1, wherein thehistorical network behavior data includes data identifying one or moreof: bandwidth utilizations by the network, transport latenciesassociated with the network, packet latencies associated with thenetwork, or packet jitters associated with the network.
 3. The method ofclaim 1, wherein the historical weather data includes data identifyingone or more of: radar associated with the geographical location, windspeeds and directions associated with the geographical location,precipitation amounts associated with the geographical location,lightning strikes associated with the geographical location, or a stormassociated with the geographical location.
 4. The method of claim 1,wherein training the correlation model, with the historical networkbehavior data, the historical weather data, and the historical actiondata, to generate the trained correlation model comprises: training thetrained correlation model to predict power outages for the geographicallocation based on the historical weather data; training the trainedcorrelation model to predict anticipated behaviors of the network basedon the predicted power outages; and training the trained correlationmodel to predict responses to the predicted power outages based on thepredicted anticipated behaviors of the network.
 5. The method of claim1, wherein the correlation model includes a deep learning model.
 6. Themethod of claim 1, wherein processing the weather event forecast, withthe trained correlation model, to determine the anticipated behavior ofthe network in response to the weather event forecast comprises:determining that the anticipated behavior of the network is associatedwith a power outage for the geographical location when the anticipatedbehavior of the network satisfies a threshold and not when theanticipated behavior of the network fails to satisfy the threshold. 7.The method of claim 1, wherein processing the weather event forecast,with the trained correlation model, to determine the anticipatedbehavior of the network in response to the weather event forecastcomprises: determining that the anticipated behavior of the network isassociated with a power outage for the geographical location; analyzingnetwork utilization data associated with one or more other networksgeographically located proximate to the network; determining an extentof the power outage based on analyzing the network utilization data; anddetermining the anticipated behavior of the network based on the extentof the power outage.
 8. A device, comprising: one or more processorsconfigured to: receive a weather event forecast for a geographicallocation associated with a network that includes network devices;process the weather event forecast, with a correlation model, todetermine an anticipated behavior of the network in response to theweather event forecast, wherein the correlation model has been trainedbased on one or more of: historical network behavior data associatedwith the network, historical weather data associated with thegeographical location, or historical action data identifying historicalactions taken for the network in response to the historical weatherdata; process data identifying the anticipated behavior of the network,with the correlation model, to identify one or more actions to take inresponse to the anticipated behavior; and perform the one or moreactions.
 9. The device of claim 8, wherein the one or more processors,when performing the one or more actions, are configured to one or moreof: cause one or more additional network devices to be temporarilyallocated for the geographical location; cause an autonomous vehiclewith a network device to be dispatched to the geographical location;cause a technician to be dispatched to the geographical location; ordetermine a power outage signature based on the anticipated behavior ofthe network and utilize the power outage signature as an alertmechanism.
 10. The device of claim 8, wherein the one or moreprocessors, when performing the one or more actions, are configured to:generate a notification about the anticipated behavior of the network;and provide the notification to users of the network located in thegeographical location.
 11. The device of claim 8, wherein the one ormore processors, when performing the one or more actions, are configuredto: determine a network capacity requirement for the network based onthe anticipated behavior of the network; determine an upgrade to thenetwork based on the network capacity requirement; and cause the upgradeto the network to be implemented.
 12. The device of claim 8, wherein theone or more processors, when performing the one or more actions, areconfigured to one or more of: cause an order to be placed for a newnetwork device of the network based on the anticipated behavior of thenetwork; adjust one or more parameters of one or more of the networkdevices based on the anticipated behavior of the network; retrain thecorrelation model based on the anticipated behavior of the network; orprioritize select users of the network located in the geographicallocation and that have paid for latency sensitive services.
 13. Thedevice of claim 8, wherein the network devices include one or more of: acellular-site router, an eNodeB, a gNodeB, an aggregation router, or adevice for a core network.
 14. The device of claim 8, wherein thehistorical network behavior data and the historical weather data areassociated with a common time period.
 15. A non-transitorycomputer-readable medium storing a set of instructions, the set ofinstructions comprising: one or more instructions that, when executed byone or more processors of a device, cause the device to: receivehistorical network behavior data associated with a network that includesnetwork devices; receive historical weather data associated with ageographical location associated with the network; train a correlationmodel, with the historical network behavior data and the historicalweather data, to generate a trained correlation model; receive a weatherevent forecast associated with the geographical location; process theweather event forecast, with the trained correlation model, to determinean anticipated behavior of the network in response to the weather eventforecast; and perform one or more actions in response to the anticipatedbehavior of the network.
 16. The non-transitory computer-readable mediumof claim 15, wherein the one or more instructions, that cause the deviceto train the correlation model, with the historical network behaviordata and the historical weather data, to generate the trainedcorrelation model, cause the device to: train a first correlation modelto predict power outages for the geographical location based on thehistorical weather data; and train a second correlation model to predictanticipated behaviors of the network based on the predicted poweroutages.
 17. The non-transitory computer-readable medium of claim 15,wherein the one or more instructions, that cause the device to processthe weather event forecast, with the trained correlation model, todetermine the anticipated behavior of the network in response to theweather event forecast, cause the device to: determine that theanticipated behavior of the network is associated with a power outagefor the geographical location when the anticipated behavior of thenetwork satisfies a threshold.
 18. The non-transitory computer-readablemedium of claim 15, wherein the one or more instructions, that cause thedevice to perform the one or more actions, cause the device to one ormore of: cause one or more additional network devices to be temporarilyallocated for the geographical location; cause an autonomous vehiclewith a network device to be dispatched to the geographical location; orcause a technician to be dispatched to the geographical location. 19.The non-transitory computer-readable medium of claim 15, wherein the oneor more instructions, that cause the device to perform the one or moreactions, cause the device to: determine a network capacity requirementfor the network based on the anticipated behavior of the network;determine an upgrade to the network based on the network capacityrequirement; and cause the upgrade to the network to be implemented. 20.The non-transitory computer-readable medium of claim 15, wherein the oneor more instructions, that cause the device to perform the one or moreactions, cause the device to one or more of: cause an order to be placedfor a new network device of the network based on the anticipatedbehavior of the network; adjust one or more parameters of one or more ofthe network devices based on the anticipated behavior of the network; orretrain the correlation model based on the anticipated behavior of thenetwork.